I was scrolling LinkedIn a while back when something made me stop and go back. I had that specific feeling of having just read something, but I couldn't place where. Turns out I'd read it seconds earlier. From a different company.
Two competitors I follow had posted nearly identical copy. Same message, same structure, same emojis in the same spots. If you work in marketing it's funny. If you're a potential buyer trying to choose between them, it's a different kind of signal: that neither company had anything distinct enough to say.
That moment crystallized something I'd been watching for a while. It's not that people are using AI. It's how they're using it. And most of the time, they're using it for the weakest possible version of what it can do.
The Spreadsheet That Taught Me the Better Use
For years before AI became part of everyday workflow, I kept a competitor tracker in a spreadsheet. New hires, customer logos, product announcements, trade shows, feature releases: anything that helped me spot patterns, momentum, and gaps in the market. It was manual and time-consuming, but it gave me something most teams didn't have: a running picture of what was actually happening versus what people said was happening.
At the time I worked in a technical industry that helped power the internet, and yet many of the companies in it still ran on trade show leads and industry relationships as primary growth channels. I became a hunter of information almost by default, because the contrast between how modern the technology was and how traditional the go-to-market strategies remained was always striking.
That spreadsheet was useful because it gave me a place to collect signals. But the real insight was in the patterns: which competitors were quietly gaining momentum, which ones were shifting their positioning, where gaps were opening up that no one else seemed to notice yet. Pulling those patterns out manually took time and attention that was better spent acting on them.
That's where AI changes the equation entirely. Not in writing the post. In seeing what the data is telling you before you decide what to say.
What AI Is Actually Good For
The better use of AI isn't faster output. It's better intelligence.
Take that same competitor tracker. Instead of reading through rows of updates to find the signal yourself, you feed the data to AI and ask it to do the pattern recognition: which companies are increasing their event presence, who's adding new functionality, who's expanding partnerships, which patterns suggest a shift in positioning or a new market bet. What would have taken hours of careful reading becomes a ten-minute analysis that surfaces the things worth paying attention to.
From there, you build a simple workflow. Competitor updates get collected weekly, organized in a spreadsheet or database, analyzed by AI, and turned into a short summary for whoever needs it. Not a bloated deck. A useful brief covering what changed, why it matters, what's worth watching, and where opportunities may be opening up.
The same logic applies to internal decision-making. AI can categorize messy market information, summarize patterns across data sources, cluster recurring themes from customer feedback, compare feature sets across competitors, or draft a weekly snapshot based on changes in activity. It reduces the time a team spends manually sorting information and increases the time available to actually decide what to do with it.
That's the shortcut worth taking. Not faster filler. Better intelligence.
The Problem With Copy-and-Paste AI
When both companies in that LinkedIn moment published the same post, someone had typed a lazy prompt into a tool, copied the output, and hit publish without a second thought. No refinement. No strategy. No awareness that if everyone is using the same tools in the same way, the output is inevitably going to collapse into sameness.
From a competitor standpoint it was an easy observation. From the perspective of a buyer trying to make a decision, it's more consequential. Two companies that sound interchangeable start to feel interchangeable. And when trust and differentiation are what separate a yes from a pass, sounding like everyone else is an expensive place to be.
The irony is that the same tool powering that forgettable post could have been used to surface the competitive insight that made the post worth writing in the first place. AI can tell you what your competitors are doing, where they're investing, what they're not saying, and where the market is moving. That's the information that produces a genuine point of view. The point of view is what makes the post worth reading.
What to Actually Ask AI to Do
If you track competitor activity anywhere (a spreadsheet, a Notion doc, an Airtable base) the workflow is straightforward. Collect the right inputs consistently: new hires, product updates, events, partnerships, messaging shifts, content themes, ad activity, customer wins. Keep it in one place. Then instead of reading row by row, ask AI to find the patterns.
Ask it what momentum looks like across the set of companies you're watching. Ask it where positioning appears to be shifting. Ask it what's missing from every competitor's narrative that your company could own. Ask it to draft a weekly summary of what changed and why it matters, short enough that someone will actually read it.
Once the structure works, tools like Zapier or Make can automate the collection and trigger the recurring summary so the intelligence arrives without the manual effort.
That's AI doing work that compounds. Every week the picture gets sharper. Every quarter the patterns become clearer. And the team making decisions is working from a more current, more complete read of the market than they'd have any other way.
When everyone else is asking AI to write the post, the smarter move is asking it to tell you what everyone else is missing.
Want more practical approaches like this? Explore my curated library of AI tools, prompts, and workflows at resources.taneilcurrie.com